A neural operator-based surrogate solver for free-form electromagnetic inverse design

Yannick Augenstein; Taavi Repän; Carsten Rockstuhl

doi:10.1021/acsphotonics.3c00156

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arXiv · 2302.01934

A neural operator-based surrogate solver for free-form electromagnetic inverse design

Yannick Augenstein·Taavi Repän·Carsten Rockstuhl

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Abstract

Neural operators have emerged as a powerful tool for solving partial differential equations in the context of scientific machine learning. Here, we implement and train a modified Fourier neural operator as a surrogate solver for electromagnetic scattering problems and compare its data efficiency to existing methods. We further demonstrate its application to the gradient-based nanophotonic inverse design of free-form, fully three-dimensional electromagnetic scatterers, an area that has so far eluded the application of deep learning techniques.

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physics.comp-ph cs.LG physics.optics

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Yannick Augenstein, Taavi Repän, Carsten Rockstuhl. 2023-02-04. A neural operator-based surrogate solver for free-form electromagnetic inverse design. https://doi.org/10.1021/acsphotonics.3c00156

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